Understand the relationship between reinforcement learning and psychology

Implement 17 different reinforcement learning algorithms

Requirements

Calculus

Probability

Markov Models

The Numpy Stack

Have experience with at least a few supervised machine learning methods

Gradient descent

Good object-oriented programming skills

Description

When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.

These tasks are pretty trivial compared to what we think of AIs doing – playing chess and Go, driving cars, and beating video games at a superhuman level.

Reinforcement learning has recently become popular for doing all of that and more.

Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn’t been until recently that we’ve been able to observe first hand the amazing results that are possible.

In 2016 we saw Google’sAlphaGo beat the world Champion in Go.

We saw AIs playing video games like Doom and Super Mario.

Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.

If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.

Learning about supervised and unsupervised machine learning is no small feat. To date I have over SIXTEEN (16!) courses just on those topics alone.

And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.

It’s led to new and amazing insights both in behavioral psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true general artificial intelligence.

What’s covered in this course?

The multi-armed bandit problem and the explore-exploit dilemma

Ways to calculate means and moving averages and their relationship to stochastic gradient descent

Markov Decision Processes (MDPs)

Dynamic Programming

Monte Carlo

Temporal Difference (TD) Learning

Approximation Methods (i.e. how to plug in a deep neural network or other differentiable model into your RL algorithm)

If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.

See you in class!

NOTES:

All the code for this course can be downloaded from my github:

/lazyprogrammer/machine_learning_examples

In the directory: rl

Make sure you always “git pull” so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

Calculus

Probability

Object-oriented programming

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations

Linear regression

Gradient descent

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don’t, I guarantee it will just look like gibberish.